论著摘要 |【多模态】局部异质性梯度取向的共现-新的影像组学描述符(双语版)

2017-09-20 10:25:27 admin 11

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

发表日期: 2016.11.22   来源: Scientific reports, 2016, 6: 37241.

作者

Prateek Prasanna, Pallavi Tiwari, Anant Madabhushi

作者介绍:

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44120, USA

摘要

本文中,我们介绍了一个新的影像组学描述符,局部异质性梯度取向的共现(CoLlAGe),来捕捉在良性的和病理表现之间在常规解剖学图像中视觉上无法区分的微小差异。CoLlAGe旨在捕捉并利用在体素水平梯度取向上的异质性的不同来区分类似的表型。CoLlAGe涉及到对每个图像体素赋予一个与每体素周围的梯度取向的共现矩阵相关联的熵值。而CoLlAGe的原理是基于这样一个假设:良性的和病理条件下,虽然组织学解剖图像特征看似相同,但他们的局部的熵模式可能有所不同,反过来有反应了组织微体系结构中微小的局部差异。我们展示了CoLlAGe在三个挑战性的临床分类问题中的作用:区分(1) 来自42例脑肿瘤患者复发肿瘤T1-w MRI数据中的辐射坏死,辐射治疗中一种良性而又混杂的效应,(2) 65个研究的DCE-MRI中乳腺癌的不同分子亚型,(3) 120例真菌(肉芽肿) 感染导致的非小细胞肺癌的CT平扫研究。对于这些分类问题中的每一个,CoLlAGE与随机森林分类器结合,表现得都优于目前最先进的影像组学描述符。

Abstract

In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe’s utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations). 

 

阅读原文:doi:10.1038/srep37241 

 


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